1. Field of the Invention
The present invention relates to the field of computer graphics and, in particular, to a system and method for camera calibration.
2. Description of the Related Art
Recently there has been an increasing demand for three-dimensional (3D) face models. The movie industry relies more and more on computer graphics (CG) to place human actors in situations that are physically not feasible. In some situations, the actor is completely replaced by a corresponding virtual counterpart since the required shots would endanger the actor.
To integrate the actors or their CG representations seamlessly, light and shadows cast from other objects must be matched. Conventional approaches using coarse facial models are not sufficient since the human eye is trained to read faces, so even subtle imperfections are spotted immediately. Also, secondary effects, such as wrinkle formation, are especially hard and tedious to create for an animator, but these secondary effects are essential for natural face appearance.
Physical simulation is currently being investigated for facial capture but is very difficult to implement since the human face is a highly complex and non-linear structure. Currently, the only practical option is to acquire a model of the face using 3D capture. The acquired models can be either integrated directly into a movie or can be used to control other faces. In addition, the movie industry is not the only industry that demands realistic face models. Computer games have a demand for virtual characters. Also, medical science has an interest in such models.
Camera calibration is an important procedure for any image-based 3D shape reconstruction of objects. Camera calibration is the process of determining parameters for the one or more cameras that capture a scene. Typically, camera calibration parameters include intrinsic parameters and extrinsic parameters. Typical examples of intrinsic parameters include focal length, image format, principal point, and/or lens distortion. The extrinsic parameters define the position and orientation of the camera in world space coordinates relative to the subject being captured.
Most 3D reconstruction techniques require the cameras to be calibrated to generate accurately reconstructed models. Conventional approaches to camera calibration involve a checkerboard pattern. The checkerboard pattern is placed at different angles relative to a camera and a sequence of images is captured by the camera. An algorithm then detects the grid pattern in the images captured by the camera and computes the intrinsic and extrinsic parameters based on the detected grid patterns. The checkerboard approach works well for a few cameras, e.g., one or two cameras, but this approach breaks down for multi-view settings due to mutual visibility constraints. For example, when the goal is to capture a subject from all sides, then cameras need to be placed all around the subject. With the checkerboard approach, there is no way to see the checkerboard from every camera. Previous approaches to calibrate multiple cameras typically rely on many images (e.g., more than 500 images) and are thus cumbersome to be carried out.
As the foregoing illustrates, there is a need in the art for an improved technique for camera calibration.